Visual Prompting (VP) is an emerging and powerful technique that allows sample-efficient adaptation to downstream tasks by engineering a well-trained frozen source model. In this work, we explore the benefits of VP in constructing compelling neural network classifiers with differential privacy (DP). We explore and integrate VP into canonical DP training methods and demonstrate its simplicity and efficiency. In particular, we discover that VP in tandem with PATE, a state-of-the-art DP training method that leverages the knowledge transfer from an ensemble of teachers, achieves the state-of-the-art privacy-utility trade-off with minimum expenditure of privacy budget. Moreover, we conduct additional experiments on cross-domain image classification with a sufficient domain gap to further unveil the advantage of VP in DP. Lastly, we also conduct extensive ablation studies to validate the effectiveness and contribution of VP under DP consideration. Our code is available at (https://github.com/EzzzLi/Prompt-PATE).

翻译：视觉提示（VP）是一种新兴且强大的技术，通过利用经过良好训练的冻结源模型实现样本高效的迁移学习，以适应下游任务。本研究探讨了VP在构建具有差分隐私（DP）的强效神经网络分类器中的优势。我们探索并将VP融入经典DP训练方法中，展示了其简洁性与高效性。特别地，我们发现VP与目前最先进的DP训练方法PATE（一种利用教师集成知识迁移的技术）相结合，能够以最小隐私预算开销达到最先进的隐私-效用权衡。此外，我们基于充足域差距的跨域图像分类实验进一步揭示了VP在DP中的优势。最后，我们通过广泛的消融研究验证了VP在DP框架下的有效性与贡献。我们的代码已开源（https://github.com/EzzzLi/Prompt-PATE）。